import torch
import torch.nn as nn
import torch.nn.functional as F
class SiLU(nn.Module):  
    @staticmethod
    def forward(x):
        return x * torch.sigmoid(x)
class Hardswish(nn.Module):  
    @staticmethod
    def forward(x):
        return x * F.hardtanh(x + 3, 0., 6.) / 6.  
class Mish(nn.Module):
    @staticmethod
    def forward(x):
        return x * F.softplus(x).tanh()
class MemoryEfficientMish(nn.Module):
    class F(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x):
            ctx.save_for_backward(x)
            return x.mul(torch.tanh(F.softplus(x)))  
        @staticmethod
        def backward(ctx, grad_output):
            x = ctx.saved_tensors[0]
            sx = torch.sigmoid(x)
            fx = F.softplus(x).tanh()
            return grad_output * (fx + x * sx * (1 - fx * fx))
    def forward(self, x):
        return self.F.apply(x)
class FReLU(nn.Module):
    def __init__(self, c1, k=3):  
        super().__init__()
        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
        self.bn = nn.BatchNorm2d(c1)
    def forward(self, x):
        return torch.max(x, self.bn(self.conv(x)))
class AconC(nn.Module):
    def __init__(self, c1):
        super().__init__()
        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
    def forward(self, x):
        dpx = (self.p1 - self.p2) * x
        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
class MetaAconC(nn.Module):
    def __init__(self, c1, k=1, s=1, r=16):  
        super().__init__()
        c2 = max(r, c1 // r)
        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
    def forward(self, x):
        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
        beta = torch.sigmoid(self.fc2(self.fc1(y)))  
        dpx = (self.p1 - self.p2) * x
        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x